After performing some optimizations on the UMAP and concluding with the best UMAP, I will plot the genes and different conditions on top, and check that they look good
You can also see that C0a is closer to C6a and the same with C6v and C0v, which is good news.
Moving onto the target genes.
GJA5 and RSPO3 are giving a good spread of the AV zonation. ODC1 and KCNE3 move onto the Tip Cell region that is where most Dll4KO cells are located. MSR1 is also giving a good indication of where are capillary artherial cells v Capillary Venous. p21 is everywhere . MKI67 and STMN1 are located in the proliferating region.
The two new conditions cluster very simillarly, so I have quickly run a DE analysis of Dll4/MycKOvJag1/Jag2/Dll1KO and saw that, for Dll4/Myc, it is HPGD, FABP5 and LGALS1 the upregulated markers and for the triple mutant it is XIST, DLL4 (obviously, I deleted it everywhere), CD59A and HSPB1.
The best ones I see are HSPB1(chaperone), GM10260(ribosomal, I do not think it is very relevant), and XIST(X chromosome silencing, only seen on female mice, both in the triple mutant and only one on Dll4/MycKO. The rest of the mice were male).
There are not that many DE genes among the two conditions, only 447 between both groups. However I can see WNT2, ODC1, p21, PDGFB, LTBP4, CD34 and EDNRB
To make sure that the groups have been rightly assigned I will check the KO genes + a few markers
Jag1 and Dll1 look good in order to confirm the Jag1/Jag2/Dll1 group. Dll4 expressing cells have been taken out from Dll4/MycKO (full homogeneous purple color) and both myc and odc1 have control levels similar to the COntrol Group.
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
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## Matrix products: default
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## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United Kingdom.1252 LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C LC_TIME=English_United Kingdom.1252
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] patchwork_1.1.1 yaml_2.2.1 rmarkdown_2.11 ggplot2_3.3.5 SeuratObject_4.0.2 Seurat_4.0.3 knitr_1.34 BiocStyle_2.20.2
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_2.0-2 deldir_0.2-10 ellipsis_0.3.2 ggridges_0.5.3 spatstat.data_2.1-0 farver_2.1.0 leiden_0.3.9 listenv_0.8.0 ggrepel_0.9.1 fansi_0.5.0 codetools_0.2-18 splines_4.1.0 polyclip_1.10-0 jsonlite_1.7.2 ica_1.0-2 cluster_2.1.2 png_0.1-7 uwot_0.1.10 shiny_1.7.1 sctransform_0.3.2 spatstat.sparse_2.0-0 BiocManager_1.30.16 compiler_4.1.0 httr_1.4.2 Matrix_1.3-3 fastmap_1.1.0 lazyeval_0.2.2 later_1.2.0 htmltools_0.5.2 tools_4.1.0 igraph_1.2.6 gtable_0.3.0 glue_1.4.2 RANN_2.6.1 reshape2_1.4.4 dplyr_1.0.7 Rcpp_1.0.7 scattermore_0.7 jquerylib_0.1.4 vctrs_0.3.8 nlme_3.1-152 lmtest_0.9-38 xfun_0.26 stringr_1.4.0 globals_0.14.0 mime_0.11 miniUI_0.1.1.1 lifecycle_1.0.1 irlba_2.3.3 goftest_1.2-2 future_1.23.0 MASS_7.3-54 zoo_1.8-9 scales_1.1.1 spatstat.core_2.3-0 promises_1.2.0.1 spatstat.utils_2.2-0 parallel_4.1.0 RColorBrewer_1.1-2 reticulate_1.20 pbapply_1.5-0 gridExtra_2.3 sass_0.4.0 rpart_4.1-15 stringi_1.7.3 highr_0.9 rlang_0.4.11 pkgconfig_2.0.3 matrixStats_0.60.0 evaluate_0.14 lattice_0.20-44 ROCR_1.0-11 purrr_0.3.4 tensor_1.5 labeling_0.4.2 htmlwidgets_1.5.4 cowplot_1.1.1 tidyselect_1.1.1 parallelly_1.28.1 RcppAnnoy_0.0.19 plyr_1.8.6 magrittr_2.0.1 bookdown_0.24 R6_2.5.1 magick_2.7.3 generics_0.1.1 withr_2.4.2 pillar_1.6.4 mgcv_1.8-35 fitdistrplus_1.1-6 survival_3.2-11 abind_1.4-5 tibble_3.1.3 future.apply_1.8.1 crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2 spatstat.geom_2.2-2 plotly_4.10.0 grid_4.1.0 data.table_1.14.0 digest_0.6.27 xtable_1.8-4 tidyr_1.1.3 httpuv_1.6.1 munsell_0.5.0 viridisLite_0.4.0 bslib_0.3.1